55,424 Works

Study Design and Analysis in Epidemiology: Where does modeling fit?

Steven Bellan
An introductory lecture on study design and analysis in classical epidemiology with an explanation of how mathematical modeling complements classical epidemiology.

Introduction to Thinking About Data II

James C Scott
An introduction to thinking about bias, confounding and random error when observing data.

Public Health, Epidemiology and Models

James C Scott
A lecture on the history and role of models in Public Health and Epidemiology.

The SIR Model Family

Jonathan Dushoff
An introduction to the susceptible-infected-recovered compartmental model of infectious disease transmission.

The SIR Model Family

Jonathan Dushoff
An introduction to the susceptible-infected-recovered compartmental model of infectious disease transmission.

Introduction to Dynamic Modeling of Infectious Diseases

Steven Bellan & Juliet Pulliam
An introduction to infectious disease transmission modeling. Goals are for audience to be able to:
1. Understand the natural history of infection
2. Explain SIR model formulation Define the basic reproduction number (R0)
3. Calculate vaccinated proportion needed for pathogen elimination
4.Explain the effect of demography on epidemics

Introduction to Thinking About Data II

James C Scott
An introduction to thinking about bias, confounding and random error when observing data.

International Clinics on Infectious Disease Dynamics and Data

Steven Bellan, Rebecca Borchering, Jonathan Dushoff, John Hargrove, Calistus Ngonghala, Travis Porco, James C Scott, Brian G Williams, Cari Van Schalkwyk & Juliet Pulliam
This collection contains pedagogic material developed by the International Clinics on Infectious Disease Dynamics and Data (ICI3D). Through annual Clinics in South Africa and the United States, the ICI3D program trains junior researchers from the US and Africa to conduct integrative research in infectious disease dynamics and to communicate their questions, methods, and findings across disciplinary boundaries.

Study Design and Analysis in Epidemiology: Where does modeling fit?

Steven Bellan
An introductory lecture on study design and analysis in classical epidemiology with an explanation of how mathematical modeling complements classical epidemiology.

The SIR Model Family

Jonathan Dushoff
An introduction to the susceptible-infected-recovered compartmental model of infectious disease transmission.

Introduction to Thinking About Data I

Brian G Williams
An introductory lecture on how to identify patterns in data and use them to generate and test hypotheses.

Introduction to Thinking About Data I

Brian G Williams
An introductory lecture on how to identify patterns in data and use them to generate and test hypotheses.

Introduction to Infectious Disease Data

Juliet Pulliam & Steven Bellan
Introduction the types of infectious disease data and their collection.

Mathematical Assumptions of Simple ODE models

Juliet Pulliam
This lecture explains some of the implicit assumptions in ordinary differential equation models, as applied to infectious diseases.

Basic stochastic simulation models

Rebecca Borchering & Juliet Pulliam
An introduction to various stochastic modeling frameworks as applied to the modeling of infectious disease transmission.

Heterogeneity, Contact Patterns and Modeling Options

Jonathan Dushoff
A lecture on how heterogeneity in infectivity, susceptibility, and contact patterns affect the transmission dynamics and control of infectious diseases.

Introduction to Models and Data: HIV in Harare, Zimbabwe

John Hargrove
This lecture explores the plausibility of different causal mechanisms as explanations for the decline in HIV prevalence in Harare using mathematical transmission models.

Introduction to Statistical Philosophy

Jonathan Dushoff
This lecture presents a pragmatic statistical philosophy, including both frequentist and Bayesian ideas as well as providing careful definitions of inference, hypothesis testing, and P values.

Introduction to Likelihood

Steven Bellan
This lecture provides a clear and intuitive visual explanation of P value- and maximum likelihood-based inference and confidence interval derivation, using a simple example.

Study Design and Analysis in Epidemiology II: Randomized Controlled Trials

Travis C Porco
An introduction to RCT design with a discussion of epidemiological, statistical and ethical implications. An example trial to test mass drug administration for trachoma is discussed.

Likelihood Fitting and Dynamic Models I: Dynamic Model Fitting and Inference Robustness

Juliet Pulliam & Steven Bellan
An introduction to fitting of mechanistic transmission models to time series data using maximum likelihood estimation along with a discussion of model (or result) robustness.

Doing Science

Brian G Williams
A discussion of the scientific process told through the history of physics and and astrophysics.

Likelihood Fitting and Dynamic Models II

Jonathan Dushoff
The second introductory lecture in a series on how to fit mechanistic disease transmission models to time series data. This lecture focuses on appropriate data transformations when fitting (log, logistic), the difference between observation and process error (noise), identifiability, and how to report uncertainty in results.

Public Health, Epidemiology and Models

James C Scott
A lecture on the history and role of models in Public Health and Epidemiology.

Introduction to Thinking About Data II

James C Scott
An introduction to thinking about bias, confounding and random error when observing data.

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Publication Year

  • 2017
    55,424